State Estimation for Discrete Neural Networks with False Data Injection Attacks under Weighted Try-Once-Discard Protocol

This paper investigates the state estimation problem for a class of discrete neural networks with time-varying delays and false data injection attacks under weighted try-once-discard (WTOD) protocol. The false data injection attacks are modelled by using the Bernoulli distributed random variable. The measurements are transmitted according to the WTOD protocol in order to reduce unnecessary data transmission and alleviate the bandwidth pressure. A state estimator is designed based on the obtained measurement. By utilizing Lyapunov stability theory, sufficient conditions are given to ensure the exponentially ultimate boundedness in the mean square of error dynamics system. Then, by combining with the linear matrix inequalities (LMIs) technology, the design method of estimator parameter is given. Finally, a numerical example is provided to testify the validity of the proposed state estimation approach.